Motor Cortical Activity During Drawing Movements: Population Representation During Lemniscate Tracing

1999 ◽  
Vol 82 (5) ◽  
pp. 2705-2718 ◽  
Author(s):  
Andrew B. Schwartz ◽  
Daniel W. Moran
Keyword(s):  
Prediction Interval ◽  
Single Cells ◽  
Premotor Cortex ◽  
Neural Representation ◽  
Movement Speed ◽  
Movement Direction ◽  
Population Vector ◽  
Vector Length ◽  
Drawing Task ◽  
Spatial Aspect

Activity was recorded extracellularly from single cells in motor and premotor cortex as monkeys traced figure-eights on a touch-sensitive computer monitor using the index finger. Each unit was recorded individually, and the responses collected from four hemispheres (3 primary motor and 1 dorsal premotor) were analyzed as a population. Population vectors constructed from this activity accurately and isomorphically represented the shape of the drawn figures showing that they represent the spatial aspect of the task well. These observations were extended by examining the temporal relation between this neural representation and finger displacement. Movements generated during this task were made in four kinematic segments. This segmentation was clearly evident in a time series of population vectors. In addition, the [Formula: see text] power law described for human drawing was also evident in the neural correlate of the monkey hand trajectory. Movement direction and speed changed continuously during the task. Within each segment, speed and direction changed reciprocally. The prediction interval between the population vector and movement direction increased in the middle of the segments where curvature was high, but decreased in straight portions at the beginning and end of each segment. In contrast to direction, prediction intervals between the movement speed and population vector length were near-constant with only a modest modulation in each segment. Population vectors predicted direction (vector angle) and speed (vector length) throughout the drawing task. Joint angular velocity and arm muscle EMG were well correlated to hand direction, suggesting that kinematic and kinetic parameters are correlated in these tasks.

scholarly journals Motor Cortical Activity During Drawing Movements: Population Representation During Spiral Tracing

1999 ◽  
Vol 82 (5) ◽  
pp. 2693-2704 ◽  
Author(s):  
Daniel W. Moran ◽  
Andrew B. Schwartz
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Prediction Interval ◽  
Premotor Cortex ◽  
Cortical Activity ◽  
Simultaneous Action ◽  
Emg Activity ◽  
Radius Of Curvature ◽  
Time Interval ◽  
Population Vector

Monkeys traced spirals on a planar surface as unitary activity was recorded from either premotor or primary motor cortex. Using the population vector algorithm, the hand's trajectory could be accurately visualized with the cortical activity throughout the task. The time interval between this prediction and the corresponding movement varied linearly with the instantaneous radius of curvature; the prediction interval was longer when the path of the finger was more curved (smaller radius). The intervals in the premotor cortex fell into two groups, whereas those in the primary motor cortex formed a single group. This suggests that the change in prediction interval is a property of a single population in primary motor cortex, with the possibility that this outcome is due to the different properties generated by the simultaneous action of separate subpopulations in premotor cortex. Electromyographic (EMG) activity and joint kinematics were also measured in this task. These parameters varied harmonically throughout the task with many of the same characteristics as those of single cortical cells. Neither the lags between joint-angular velocities and hand velocity nor the lags between EMG and hand velocity could explain the changes in prediction interval between cortical activity and hand velocity. The simple spatial and temporal relationship between cortical activity and finger trajectory suggests that the figural aspects of this task are major components of cortical activity.

Changes in motor cortex activity during reaching movements with similar hand paths but different arm postures

1995 ◽  
Vol 73 (6) ◽  
pp. 2563-2567 ◽  
Author(s):  
S. H. Scott ◽  
J. F. Kalaska
Keyword(s):  
Motor Cortex ◽  
Sagittal Plane ◽  
Single Cells ◽  
Reaching Movements ◽  
Movement Direction ◽  
Tonic Activity ◽  
Population Vector ◽  
Significant Difference ◽  
Direction Of Movement ◽  
Kinematics And Kinetics

1. Neuronal activity was recorded in the motor cortex of a monkey that performed reaching movements with the use of two different arm postures. In the first posture (control), the monkey used its natural arm orientation, approximately in the sagittal plane. In the second posture (abducted), the monkey had to adduct its elbow nearly to shoulder level to grasp the handle. The path of the hand between targets was similar in both arm postures, but the joint kinematics and kinetics were different. 2. In both postures, the activity of single cells was often broadly tuned with movement direction and static arm posture over the targets. In a large proportion of cells, either the level of tonic activity, the directional tuning, or both, varied between the two postures during the movement and target hold periods. 3. For most directions of movement, there was a statistically significant difference in the direction of the population vector for the two arm postures. Furthermore, whereas the population vector tended to point in the direction of movement for the control posture, there was a poorer correspondence between the direction of movement and the population vector for the abducted posture. These observed changes are inconsistent with the notion that the motor cortex encodes purely hand trajectory in space.

scholarly journals Statistical Characteristics of Unsteady Storms in Radar Observations for the Beijing–Tianjin Region

2015 ◽  
Vol 54 (1) ◽  
pp. 106-116 ◽  
Author(s):  
Yu Wang ◽  
Hong-Qing Wang ◽  
Lei Han ◽  
Yin-Jing Lin ◽  
Yan Zhang
Keyword(s):  
Warm Season ◽  
Environmental Parameters ◽  
Statistical Characteristics ◽  
Movement Speed ◽  
Movement Direction ◽  
Upper Troposphere ◽  
Maximum Value ◽  
Dynamic Factors ◽  
The Mean ◽  
Wind Profiles

AbstractThis study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

scholarly journals Using experience to improve: how errors shape behavior and brain activity in monkeys

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5395
Author(s):  
Jose L. Pardo-Vazquez ◽  
Carlos Acuña
Keyword(s):  
Neuronal Activity ◽  
Decision Process ◽  
Brain Activity ◽  
Single Cells ◽  
Premotor Cortex ◽  
Strong Support ◽  
Behavioral Performance ◽  
Previous Trial ◽  
Current Trial ◽  
Future Behavior

Previous works have shown that neurons from the ventral premotor cortex (PMv) represent several elements of perceptual decisions. One of the most striking findings was that, after the outcome of the choice is known, neurons from PMv encode all the information necessary for evaluating the decision process. These results prompted us to suggest that this cortical area could be involved in shaping future behavior. In this work, we have characterized neuronal activity and behavioral performance as a function of the outcome of the previous trial. We found that the outcome of the immediately previous trial (n−1) significantly changes, in the current trial (n), the activity of single cells and behavioral performance. The outcome of trial n−2, however, does not affect either behavior or neuronal activity. Moreover, the outcome of difficult trials had a greater impact on performance and recruited more PMv neurons than the outcome of easy trials. These results give strong support to our suggestion that PMv neurons evaluate the decision process and use this information to modify future behavior.

scholarly journals The left ventral premotor cortex is involved in hand shaping for intransitive gestures: evidence from a two-person imitation experiment

2018 ◽  
Vol 5 (10) ◽  
pp. 181356 ◽  
Author(s):  
Arran T. Reader ◽  
Nicholas P. Holmes
Keyword(s):  
Repetitive Transcranial Magnetic Stimulation ◽  
Action Observation ◽  
Premotor Cortex ◽  
Control Site ◽  
Movement Speed ◽  
Hand Shaping ◽  
Ventral Premotor Cortex ◽  
Action Imitation ◽  
Control Research

The ventral premotor cortex (PMv) is involved in grasping and object manipulation, while the dorsal premotor cortex (PMd) has been suggested to play a role in reaching and action selection. These areas have also been associated with action imitation, but their relative roles in different types of action imitation are unclear. We examined the role of the left PMv and PMd in meaningful and meaningless action imitation by using repetitive transcranial magnetic stimulation (rTMS). Participants imitated meaningful and meaningless actions performed by a confederate actor while both individuals were motion-tracked. rTMS was applied over the left PMv, left PMd or a vertex control site during action observation or imitation. Digit velocity was significantly greater following stimulation over the PMv during imitation compared with stimulation over the PMv during observation, regardless of action meaning. Similar effects were not observed over the PMd or vertex. In addition, stimulation over the PMv increased finger movement speed in a (non-imitative) finger–thumb opposition task. We suggest that claims regarding the role of the PMv in object-directed hand shaping may stem from the prevalence of object-directed designs in motor control research. Our results indicate that the PMv may have a broader role in ‘target-directed’ hand shaping, whereby different areas of the hand are considered targets to act upon during intransitive gesturing.

scholarly journals Movement speed is biased by prior experience

2014 ◽  
Vol 111 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Ulrike Hammerbeck ◽  
Nada Yousif ◽  
Richard Greenwood ◽  
John C. Rothwell ◽  
Jörn Diedrichsen
Keyword(s):  
Training Group ◽  
Movement Speed ◽  
Movement Direction ◽  
Task Constraints ◽  
Reaching Task ◽  
Optimal Balance ◽  
Fast Training ◽  
Human Participants ◽  
Selection Of ◽  
Dependent Mechanism

How does the motor system choose the speed for any given movement? Many current models assume a process that finds the optimal balance between the costs of moving fast and the rewards of achieving the goal. Here, we show that such models also need to take into account a prior representation of preferred movement speed, which can be changed by prolonged practice. In a time-constrained reaching task, human participants made 25-cm reaching movements within 300, 500, 700, or 900 ms. They were then trained for 3 days to execute the movement at either the slowest (900-ms) or fastest (300-ms) speed. When retested on the 4th day, movements executed under all four time constraints were biased toward the speed of the trained movement. In addition, trial-to-trial variation in speed of the trained movement was significantly reduced. These findings are indicative of a use-dependent mechanism that biases the selection of speed. Reduced speed variability was also associated with reduced errors in movement amplitude for the fast training group, which generalized nearly fully to a new movement direction. In contrast, changes in perpendicular error were specific to the trained direction. In sum, our results suggest the existence of a relatively stable but modifiable prior of preferred movement speed that influences the choice of movement speed under a range of task constraints.

Eye Position Effects on the Neuronal Activity of Dorsal Premotor Cortex in the Macaque Monkey

1998 ◽  
Vol 80 (3) ◽  
pp. 1132-1150 ◽  
Author(s):  
Driss Boussaoud ◽  
Christophe Jouffrais ◽  
Frank Bremmer
Keyword(s):  
Reference Frame ◽  
Neuronal Activity ◽  
Premotor Cortex ◽  
Macaque Monkey ◽  
Limb Movement ◽  
Movement Direction ◽  
Eye Position ◽  
Dorsal Premotor Cortex ◽  
Position Effects ◽  
The Brain

Boussaoud, Driss, Christophe Jouffrais, and Frank Bremmer. Eye position effects on the neuronal activity of dorsal premotor cortex in the macaque monkey. J. Neurophysiol. 80: 1132–1150, 1998. Visual inputs to the brain are mapped in a retinocentric reference frame, but the motor system plans movements in a body-centered frame. This basic observation implies that the brain must transform target coordinates from one reference frame to another. Physiological studies revealed that the posterior parietal cortex may contribute a large part of such a transformation, but the question remains as to whether the premotor areas receive visual information, from the parietal cortex, readily coded in body-centered coordinates. To answer this question, we studied dorsal premotor cortex (PMd) neurons in two monkeys while they performed a conditional visuomotor task and maintained fixation at different gaze angles. Visual stimuli were presented on a video monitor, and the monkeys made limb movements on a panel of three touch pads located at the bottom of the monitor. A trial begins when the monkey puts its hand on the central pad. Then, later in the trial, a colored cue instructed a limb movement to the left touch pad if red or to the right one if green. The cues lasted for a variable delay, the instructed delay period, and their offset served as the go signal. The fixation spot was presented at the center of the screen or at one of four peripheral locations. Because the monkey's head was restrained, peripheral fixations caused a deviation of the eyes within the orbit, but for each fixation angle, the instructional cue was presented at nine locations with constant retinocentric coordinates. After the presentation of the instructional cue, 133 PMd cells displayed a phasic discharge (signal-related activity), 157 were tonically active during the instructed delay period (set-related or preparatory activity), and 104 were active after the go signal in relation to movement (movement-related activity). A large proportion of cells showed variations of the discharge rate in relation to limb movement direction, but only modest proportions were sensitive to the cue's location (signal, 43%; set, 34%; movement, 29%). More importantly, the activity of most neurons (signal, 74%; set, 79%; movement, 79%) varied significantly (analysis of variance, P < 0.05) with orbital eye position. A regression analysis showed that the neuronal activity varied linearly with eye position along the horizontal and vertical axes and can be approximated by a two-dimensional regression plane. These data provide evidence that eye position signals modulate the neuronal activity beyond sensory areas, including those involved in visually guided reaching limb movements. Further, they show that neuronal activity related to movement preparation and execution combines at least two directional parameters: arm movement direction and gaze direction in space. It is suggested that a substantial population of PMd cells codes limb movement direction in a head-centered reference frame.

scholarly journals Neuronal Activity Related to the Visual Representation of Arm Movements in the Lateral Cerebellar Cortex

2003 ◽  
Vol 89 (3) ◽  
pp. 1223-1237 ◽  
Author(s):  
Xuguang Liu ◽  
Edwin Robertson ◽  
R. Christopher Miall
Keyword(s):  
Neuronal Activity ◽  
Cerebellar Cortex ◽  
Arm Movements ◽  
Arm Movement ◽  
Visual Outcome ◽  
Neural Representation ◽  
Movement Direction ◽  
Visually Guided ◽  
Motor Actions ◽  
Increased Activity

Testing the hypothesis that the lateral cerebellum forms a sensory representation of arm movements, we investigated cortical neuronal activity in two monkeys performing visually guided step-tracking movements with a manipulandum. A virtual target and cursor image were viewed co-planar with the manipulandum. In the normal task, manipulandum and cursor moved in the same direction; in the mirror task, the cursor was left-right reversed. In one monkey, 70- and 200-ms time delays were introduced on cursor movement. Significant task-related activity was recorded in 31 cells in one animal and 142 cells in the second: 10.2% increased activity before arm movements onset, 77.1% during arm movement, and 12.7% after the new position was reached. To test for neural representation of the visual outcome of movement, firing rate modulation was compared in normal and mirror step-tracking. Most task-related neurons (68%) showed no significant directional modulation. Of 70 directionally sensitive cells, almost one-half ( n = 34, 48%) modulated firing with a consistent cursor movement direction, many fewer responding to the manipulandum direction ( n = 9, 13%). For those “cursor-related” cells tested with delayed cursor movement, increased activity onset was time-locked to arm movement and not cursor movement, but activation duration was extended by an amount similar to the applied delay. Hence, activity returned to baseline about when the delayed cursor reached the target. We conclude that many cells in the lateral cerebellar cortex signaled the direction of cursor movement during active step-tracking. Such a predictive representation of the arm movement could be used in the guidance of visuo-motor actions.

The role of intrinsic factors in control of arm movement direction: implications from directional preferences

2011 ◽  
Vol 105 (3) ◽  
pp. 999-1010 ◽  
Author(s):  
Natalia Dounskaia ◽  
Jacob A. Goble ◽  
Wanyue Wang
Keyword(s):  
Arm Movement ◽  
Movement Direction ◽  
Revealed Preferences ◽  
Intrinsic Nature ◽  
Intrinsic Factors ◽  
Intersegmental Dynamics ◽  
Drawing Task ◽  
Dominant Tendency ◽  
Selection Of

The role of extrinsic and intrinsic factors in control of arm movement direction remains under debate. We addressed this question by investigating preferences in selection of movement direction and whether factors causing these preferences have extrinsic or intrinsic nature. An unconstrained free-stroke drawing task was used during which participants produced straight strokes on a horizontal table, choosing the direction and the beginning and end of each stroke arbitrarily. The variation of the initial arm postures across strokes provided a possibility to distinguish between the extrinsic and intrinsic origins of directional biases. Although participants were encouraged to produce strokes equally in all directions, each participant demonstrated preferences for some directions over the others. However, the preferred directions were not consistent across participants, suggesting no directional preferences in extrinsic space. Consistent biases toward certain directions were revealed in intrinsic space representing initial arm postures. Factors contributing to the revealed preferences were analyzed within the optimal control framework. The major bias was explained by a tendency predicted by the leading joint hypothesis (LJH) to minimize active interference with interaction torque generated by shoulder motion at the elbow. Some minor biases may represent movements of minimal inertial resistance or maximal kinematic manipulability. These results support a crucial role of intrinsic factors in control of the movement direction of the arm. Based on the LJH interpretation of the major bias, we hypothesize that the dominant tendency was to minimize neural effort for control of arm intersegmental dynamics. Possible organization of neural processes underlying optimal selection of movement direction is discussed.

Direct and Indirect Cooperation between Temporal and Parietal Networks for Invariant Visual Recognition

1992 ◽  
Vol 4 (1) ◽  
pp. 35-57 ◽  
Author(s):  
Isabelle Otto ◽  
Philippe Grandguillaume ◽  
Latifa Boutkhil ◽  
Yves Burnod ◽  
Emmanuel GuigonBurnod
Keyword(s):  
Receptive Field ◽  
Visual System ◽  
Visual Recognition ◽  
Single Cells ◽  
Central Zone ◽  
Object Identification ◽  
Population Coding ◽  
Significant Shift ◽  
Learning Session ◽  
Population Vector

A new type of biologically inspired multilayered network is proposed to model the properties of the primate visual system with respect to invariant visual recognition (IVR). This model is based on 10 major neurobiological and psychological constraints. The first five constraints shape the architecture and properties of the network. 1. The network model has a Y-like double-branched multilayered architecture, with one input (the retina) and two parallel outputs, the “What” and the “Where,” which model, respectively, the temporal pathway, specialized for “object” identification, and the parietal pathway specialized for “spatial” localization. 2. Four processing layers are sufficient to model the main functional steps of primate visual system that transform the retinal information into prototypes (object-centered reference frame) in the “What” branch and into an oculomotor command in the “Where” branch. 3. The distribution of receptive field sizes within and between the two functional pathways provides an appropriate tradeoff between discrimination and invariant recognition capabilities. 4. The two outputs are represented by a population coding: the ocular command is computed as a population vector in the “Where” branch and the prototypes are coded in a “semidistributed” way in the “What” branch. In the intermediate associative steps, processing units learn to associate prototypes (through feedback connections) to component features (through feedforward ones). 5. The basic processing units of the network do not model single cells but model the local neuronal circuits that combine different information flows organized in separate cortical layers. Such a biologically constrained model shows shift-invariant and size-invariant capabilities that resemble those of humans (psychological constraints): 6. During the Learning session, a set of patterns (26 capital letters and 2 geometric figures) are presented to the network: a single presentation of each pattern in one position (at the center) and with one size is sufficient to learn the corresponding prototypes (internal representations). These patterns are thus presented in widely varying new sizes and positions during the Recognition session: 7. The “What” branch of the network succeeds in immediate recognition for patterns presented in the central zone of the retina with the learned size. 8. The recognition by the “What” branch is resistant to changes in size within a limited range of variation related to the distribution of receptive field (RF) sizes in the successive processing steps of this pathway. 9. Even when ocular movements are not allowed, the recognition capabilities of the “What” branch are unaffected by changing positions around the learned one. This significant shift-invariance of the “What” branch is also related to the distribution of RF sizes. 10. When varying both sizes and locations, the “What” and the “Where” branches cooperate for recognition: the location coding in the “Where” branch can command, under the control of the “What” branch, an ocular movement efficient to reset peripheral patterns toward the central zone of the retina until successful recognition. This model results in predictions about anatomical connections and physiological interactions between temporal and parietal cortices.

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